Tax return evaluation system

ABSTRACT

Tax returns are received from one or more tax agencies. Each tax return is compared to a stored profile, and a determination is made as to whether each tax return falls within a trend. An evaluation of the tax return is generated based on the comparison and the determination of whether the tax return falls within the trend. The evaluation includes an indication of the tax returns potential to be a fraudulent tax return.

BACKGROUND

Tax returns are filed from several sources, including through the afederal or state electronic filing program, directly from taxpayers viatax preparation software or paper-based returns, or bulk-filing from taxpreparers and data-entry vendors. The government tax agency handling thefilings, such as the Internal Revenue Service (IRS) or state revenuedepartments, typically enters the information from the tax returns foreach filer into their internal database, and then human auditors mayreview the data to identify fraudulent or inaccurate returns.

The conventional auditing process, however, provides littlecollaboration or validation from other tax agencies and data sources,and as a result is less likely to capture multi-state tax fraudcampaigns. For example, a person may attempt to defraud multiple statesby filing tax returns in ten different states under a social securitynumber for a deceased person. Self-contained systems of each stateagency are not able to identify that refunds are being requested underthe same social security number for multiple different states, and maybe unable to connect with any databases that store information about thedeceased. Furthermore, this type of fraud may not be captured by thetraditional auditing process, if none of the flags are triggered. Forexample, if the fraudulent returns are each claiming a modest refund,e.g., $1500.00 or less, the returns may fall below a threshold thattriggers a flag for an audit, and the refunds may be paid.

BRIEF DESCRIPTION OF DRAWINGS

The embodiments of the invention will be described in detail in thefollowing description with reference to the following figures.

FIG. 1 illustrates a tax return evaluation system, according to anembodiment;

FIGS. 2 illustrates a method for evaluating tax returns, according to anembodiment;

FIG. 3 illustrates a method for determining a trend profile, accordingto an embodiment; and

FIG. 4 illustrates a computer system that may be used for the methodsand system, according to an embodiment.

DETAILED DESCRIPTION OF EMBODIMENTS

For simplicity and illustrative purposes, the principles of theembodiments are described by referring mainly to examples thereof. Inthe following description, numerous specific details are set forth inorder to provide a thorough understanding of the embodiments. It will beapparent however, to one of ordinary skill in the art, that theembodiments may be practiced without limitation to these specificdetails. In some instances, well known methods and structures have notbeen described in detail so as not to unnecessarily obscure theembodiments.

1. Overview

According to an embodiment, a tax return evaluation system identifiespotential taxpayer fraud by examining data fields in filed taxpayerreturns. Examples of the data fields include social security number,address, refund requested, withholding, employer ID, tax prepare ID,bank account information, adjusted gross income (AGI), etc. The datafields or information derived from the data fields (e.g., ratio ofrefund to AGI) are compared to profiles and information from multipledata sources to identify potentially fraudulent returns. The profilesare custom in that they can be created and provided by tax agencies orother entities requesting evaluation by the tax return evaluationsystem. For their profiles, each tax agency can pick the data fields andvalues for the data fields, which may be thresholds, that are consideredflags for detecting fraudulent returns. The data fields in the profilesare referred to as factors. Furthermore, an agency may have more thanone profile for detecting different types of fraud or incorrect returns.

In one embodiment, the tax return evaluation system is a central systemthat is connected to the data sources and is configured to receive andstore the profiles from each tax agency. The data sources may includepublic databases or other public data sources, collaborating governmentagencies, or an internal data source compiling information from taxreturns from multiple tax agencies. Examples of the data in the datasources include an invalid social security number database, deceasedinformation database, criminal warrants and liens database, propertyassessments database, and known fraudulent tax preparers and filers. Thetax return evaluation system receives the tax returns from the taxagencies and uses the custom profiles and information from the datasources for fraud detection. The tax return evaluation system may use aservice-oriented architecture, and may be provided in the form of aweb-based application supported by a relational database managementsystem. In other embodiments, some or all of the functionality of thetax return evaluation system can be provided as part of a system for aparticular tax agency, such as incorporated with the IRS's current taxsystem or incorporated in a state tax system. The tax agency may be agovernment agency responsible for collecting taxes.

According to an embodiment, the tax return evaluation system performs atrend analysis to identify factors that are associated with potentiallyfraudulent returns. The trend analysis may encompass an intra-trendanalysis that analyzes factors within a single return and an inter-trendanalysis that analyzes factors across multiple tax returns, which mayinclude returns from multiple states and the IRS.

Also, a scoring function may be applied to score each tax return basedon a comparison to one or more profiles, which may be the customprofiles provided by the tax agency or trend analysis profiles. A scoreis generated for each return and is used to determine whether the returnis fraudulent. Also, the trend analysis can impact the score if adetermination is made that the return falls within a trend ofpotentially fraudulent returns.

2. System Diagram

FIG. 1 illustrates a tax return evaluation system 100, according to anembodiment. The tax return evaluation system 100 includes a tax computer110, a profiles database 111, and a tax return database 112. The taxevaluation system 100 may include other well known components. The taxcomputer 110 includes one or more computer systems for evaluating taxreturns. The profiles database 111 stores profiles, which may bereceived from tax agencies 101 a-n or other entities. The tax returndatabase 112 stores received tax returns to be evaluated. The taxagencies 101 a-n may include state tax agencies, and/or the IRS. The taxreturn evaluation system 100 is also connected to data sources 102 a-f.These are data sources that provide information that can be used forevaluating tax returns for fraud. The data sources 102 a-f may bepublically available data sources, private data sources or governmentdata sources.

The tax return evaluation system 100 receives tax returns from the taxagencies 101 a-n. For example, the tax agencies 101 a-n collect the taxreturns from their tax payers and send the returns to the tax returnevaluation system 100. The tax returns may be sent in batch jobs or inreal-time, as they are received by the tax agencies 101 a-n. The taxreturn database 112 stores the received tax returns.

The tax return evaluation system 100 evaluates each tax return for fraudusing the stored profiles in the profile database 111. The storedprofiles include factors for evaluating tax returns. The factors areassociated with data fields in a tax return. Examples of the factors anddata fields are social security number, tax payer ID, address, anybanking information, tax preparer ID/name/address, refund amount, AGI,withholding amount; whether the filer is a first time filer, whether theaddress is out-of-state and from a non-contiguous state, whether refundswere previously requested by the filer and how much, etc.

The factors have associated values in the profile. The values may bevalues for the factors. For example, a profile may want to evaluatenon-contiguous, out-of-state filers that request refunds between$1500.00 and $3000.00. The range between $1500.00 and $3000.00 arevalues for the refund amount factor. Non-contiguous and out-of-state arevalues for a factor consisting of address of the filer.

The tax computer 110 uses a profile for a tax agency to identify fraudfor tax returns from the jurisdiction of the agency. The profile may beretrieved from the profiles database 111. For example, values from thedata fields in each tax return are extracted. These values are comparedto values in the profile, such as the range between $1500.00 and$3000.00 for refund amount, and non-contiguous and out-of-state foraddress. If values from a tax return match the values in the profile,then an evaluation of the return for fraud is generated. An example of amatch is if the tax return includes a refund amount data field value of$2000.00, because it is in the range between $1500.00 and $3000.00specified for the refund amount factor in the profile. A data field froma tax return that is compared to a factor in a profile for matching isreferred to as a corresponding data field for the factor. Also, if avalue of a corresponding data field satisfies the value of its factor,such as the example where the data field value falls within the rangefor the factor, the factor and corresponding data field are described asmatching.

An evaluation is generated for the return that indicates the likelihoodor probability that the return is fraudulent. The evaluation variesdepending on multiple criteria, which may include number of matches, thetype of matching factors, trends, and others. Also, values from the taxreturn are compared to information from the data sources 102 a-f.Matches between the values from the tax return and the information fromthe data sources 102 a-f impact the evaluation of the tax return. Forexample, if the data source 102 a includes social security numbers fordeceased individuals, and the social security number from the tax returnmatches a social security number from the data source 102 a, then thetax return may be marked as fraudulent. In another example, the datasource 102 b includes information for people previously convicted forfraud. A match between the data source 102 b and a tax return impactsthe evaluation to indicate a greater likelihood of fraud.

Trends are also detected using the profiles. In one example, a trend isidentified if multiple values for factors in the profile match values indata fields in a tax return. If a trend is detected, the trend impactsthe evaluation of the tax return, for example, by indicating anincreased likelihood of fraud. Other types of trends that are associatedwith factors across multiple tax returns may also be detected, and trendprofiles are created for these types of trends.

As indicated above, the evaluation of a tax return indicates aprobability or likelihood of fraud. The evaluation is not necessarily a“yes” or “no” answer of whether a tax return is fraudulent, and mayindicate the degree of likelihood of fraud. In one embodiment, scoringis used to determine the evaluation of the tax return and to indicatethe degree of likelihood of fraud. A score is generated by the taxreturn evaluation system that indicates the likelihood the tax return isfraud. In one example, the score is between 1 and 100, where 100 is thehighest likelihood of fraud. Other scoring ranges may alternatively beused. A trend multiplier is used to change the score if a trend isdetected. Other criteria also impact the score. Examples of the scoringare described in further detail below.

As described above, the tax return evaluation system 100 uses profilesto evaluate tax returns for fraud. However, the tax return evaluationsystem 100 maybe used to evaluate tax returns to achieve otherobjectives. For example, fraud typically includes intentionalmisrepresentation in the tax return. The tax return evaluation systemmay be used to identify unintentional misreporting in tax returns usingprofiles. Furthermore, the profiles may be used to identify tax returnsfor auditing. For example, if a tax return matches multiple factors in aprofile, the tax return is flagged for further auditing by the taxagency.

In one embodiment, the tax return evaluation system 100 is a servicethat is accessed via the Internet. For example, each of the tax agencies101 a-n uploads tax returns to the tax return evaluation system 100 viaa web interface, and the tax returns are stored in the tax returndatabase 112. The tax return evaluation system 100 evaluates each returnand sends the evaluations to the tax agencies 101 a-n or makes theevaluations available to the tax agencies 101 a-n for downloading viathe web interface. In this embodiment, the tax return evaluation systemoperates as a central, remote system that is accessible via the Internetor other private or public networks. Also, the tax return evaluationsystem 100 is able to capture information from tax agencies, which canbe used to identify trends across multiple jurisdictions.

Because of data sensitivity issues, the tax agencies 101 a-n may notsend entire tax returns. For example, instead of receiving and storingentire tax returns, the tax return evaluation system 100 may onlyreceive predetermined line items from each return, and store those lineitems in the tax return database 112. These line items are then comparedto one or more stored profiles to evaluate each return.

In another embodiment, the tax return evaluation system 100 isincorporated in a local tax computer system of the tax agency. In thisembodiment, the tax return evaluation system 100 may not have thebenefit of accessing tax return data from other jurisdictions. However,in this embodiment, the system may be more secure in that data from taxreturns that is sent to the tax return evaluation system 100 remainsinternal to the same system that receives the tax returns from the taxpayers, and is not provided or stored with tax payer information fromother jurisdictions. However, data storage policies may be institutedfor the central tax return evaluation system embodiment to maintainconfidentiality and to keep data from different jurisdictions separatedas needed. Furthermore, in the embodiment where the tax returnevaluation system 100 operates as a central, remote system, securecommunication between the tax return evaluation system 100 and the taxagencies 101 a-n and the data sources 101 a-f as needed may be providedthrough conventional techniques, such as Secure Sockets Layer (SSL).

3. Scoring Examples

The tax return evaluation system 100 is operable to generate anevaluation for each tax return that indicates a likelihood of fraud. Inone embodiment, scoring is used to generate the evaluation. Scoring maybe based on multiple criteria, such as the number of matches foundbetween data fields (e.g., line items) in the tax return and factors ina profile, matches between the data fields and information from the datasources 102 a-f, the types of matches identified, where type isassociated with the type of factor or data source that has a match, andweights for the types of matches. The evaluation of a tax return may bea report including a score and all the pertinent matching information.

In an example, suppose the tax agency 101 a wants to identify fraudschemes from out-of-state filers. The tax agency 101 a sends a profile200 to the tax return evaluation system 100. The profile 200 includesthe following factors and associated values shown in table 1.

TABLE 1 for Profile 200 Factor Value address out-of-state,non-contiguous refund amount $1,500.00-$3,000.00 tax preparer ABC taxpreparers (same tax preparer as other returns with matching factors)

The profile 200 has three factors including address of the tax payer,refund amount, and tax preparer. The values for each factor are alsoshown. The profile 200 may also specify weights for each value of afactor, which is used to calculate a score. For example, for address, ascore of 10 is given if the address in a tax return is out-of-state butis in a contiguous state. If the address is both out-of-state but and ina non-contiguous state, the profile may specify a higher weight. Forexample, a score of 30 is given if both values are matched. If therefund amount in the tax return falls within the range of$1,500.00-$3,000.00, then a score of 25 is given for that factor basedon the assigned weighting for that factor. As multiple tax returns areevaluated, the tax return evaluation system 100 is able to identify taxpreparers filing tax returns that match the address and refund amountfactors. If the tax return being evaluated has one of these taxpreparers (e.g., ABC tax preparers) as its tax preparer then a score of35 is given. The scores for each factor are accumulated to determine afinal score for the tax return. The final score along with all thepertinent matching information is reported to the tax agency 101 a.

The final score is also changed based on whether a trend is identifiedwhen evaluating a return. The trend multiplier is an amount that ismultiplied by a score to account for an identified trend. The profilemay specify the trend multiplier. The trend multiplier may be applied tothe accumulated score to determine the final score. For example, a trendis identified for a tax return if multiple data fields match the factorsin the profile. If the tax return has an address that is out-of-stateand has a refund amount of $1,500.00 then the tax return has multiplematches. If the trend multiplier is 1.3, then 1.3 is multiplied by theaccumulated score of 35 to determine the final score of 45.5. In otherexamples, the trend multiplier may be applied only to the scores thatare for the matching factors. Also, the trend may be defined as multiplematching factors, or a majority of the factors are matched by the datafields in the tax return, or all the factors in the profile are matchedby the data fields in the tax return.

Other criteria may influence the score. Information from the datasources 102 a-f that matches data fields in the tax return may cause thescore to be increased. For example, if the data source 102 a includessocial security numbers for deceased individuals, and the socialsecurity number from the tax return matches a social security numberfrom the data source 102 a, then the tax return may be given a maximumscore. In another example, the tax return evaluation system 100 maycompile information for fraudulent returns and use the information togenerate profiles. Scores for tax returns matching these profiles areincreased.

The profiles may be updated based to detect new fraud schemes and otherincorrect tax returns. Thus, the tax return evaluation system 100 allowsfor dynamic evaluation of tax returns that allows the factors andweighting of the factors to be modified as needed.

4. Flowcharts

FIG. 2 illustrates a flowchart 200 for evaluating a tax return,according to an embodiment. The methods described herein may bedescribed with respect to the tax return evaluation system 100 shown inFIG. 1 by way of example and not limitation. The methods may bepracticed in other systems. Also, some of the steps of the methods maybe performed in different orders than shown.

At step 201, profiles are received from the tax agencies 101 a-n andstored in the profiles database 111.

At step 202, a tax return is received from a tax agency, such as the taxagency 101 a. Instead of receiving an entire tax return, a plurality ofline items (i.e., data fields) from the tax return is received. The taxreturn may be provided in batch job with several other returns or inreal time as it is received by the tax agency.

At step 203, a profile for the tax agency 101 a is identified. Forexample, a profile for the tax agency 101 a is retrieved from theprofiles database 111. A tax agency may have multiple profiles. In thatcase, the steps for evaluating the profile against the return isperformed for each of the profiles.

At step 204, the tax return evaluation system 100 compares the taxreturn to the profile. The tax return evaluation system 100 determineswhether any of the received data fields in the tax return match thefactors in the profile. If no, then the tax return is marked as properor not fraudulent at step 205. If yes, then, at step 206, an evaluationof the tax return is generated that includes an estimation of thelikelihood the tax return is fraudulent and is based on the matchingfactors. This estimation may be an intermediate evaluation that ismodified before a final evaluation is determined. For example, a scoreis determined for each matching factor. Also, weightings for the factorsmay be used to determine the scores. The scores for the factors areintermediate evaluations for the tax return.

At step 207, the tax return evaluation system 100 determines whether thetax return falls within a trend based on the comparison of the taxreturn to the factors in the profile. The trend may be defined asmultiple factors in the profile matching data fields in the tax return,or a majority of the factors matching data fields in the tax return, orall the factors matching data fields in the tax return. The tax returnis determined to fall within the trend if multiple factors in theprofile match data fields in the tax return, or if a majority of theprofiles are matched, or if all the profiles are matched, depending onhow the trend is defined.

At step 208, if the tax return falls within the trend, then one or moreevaluations determined at step 206 are modified to take intoconsideration the trend. For example, a trend multiplier is used tomodify an accumulated score calculated from the scores for each matchingfactor.

At step 209, the tax return evaluation system 100 determines whetherinformation from one or more of the data sources 102 a-f matches the taxreturn. If yes, then, at step 210, the evaluation of the tax return,such as determined by the previous steps, is modified to take intoconsideration the matching. This may include increasing the score by apredetermined amount or a predetermined multiple.

At step 211, the tax return evaluation system 100 determines whether thetax return falls within a trend profile. For example, the tax returnevaluation system 100 may generate trend profiles based on informationfrom previous returns that were considered highly likely to befraudulent. The tax return evaluation system 100 compiles the factorsthat are common to those profiles to generate a trend profile. If thetax return falls within the trend profile, then, at step 212, theevaluation of the tax return, such as determined by the previous steps,is modified to take into consideration the trend. This may include usinga trend multiplier or other predetermined value to modify the score.

At step 213, a final evaluation is determined. This may include a scoredetermined based on the previous steps. At step 214, the finalevaluation is reported to the tax agency 101 a, and may include thefinal score and any pertinent matching information.

The steps of the method 200 are repeated to evaluate each return. Itshould be noted that in some embodiments, some of the steps may beoptional. For example, a tax return may not be evaluated against a trendprofile at step 211, or if information is not available from the datasources 102 a-f, then step 209 is not performed.

FIG. 3 illustrates a method 300 for determining a trend profile,according to an embodiment. As described with respect to step 211, thetax return evaluation system 100 determines whether the tax return fallswithin a trend profile. The tax return evaluation system 100 maygenerate the trend profile based on information from previous returnsthat were considered highly likely to be fraudulent.

At step 301, the tax return evaluation system 100 identifies tax returnshighly likely to be fraudulent. This may include tax returns having ascore greater than a threshold. Tax returns from multiple tax agenciesmay be identified.

At step 302, common data fields from the tax returns identified at step301 are determined. At step 303, a trend profile is determined from thecommon data fields. For example, the tax return evaluation system 100determines that many tax returns from non-contiguous, out-of-statefilers having the same tax preparer are highly likely to be fraudulent.The data fields of address and tax preparer and the values for thefields comprising non-contiguous, out-of-state filers and the name ofthe tax preparer that is the same for the tax returns become the factorsand corresponding values for the factors in the trend profile.

At step 304, the trend profile is stored in the profiles database 111. Atrend profile may be applicable for particular returns. For example, atrend profile may be compiled from tax returns from a particular region,such as states in the northeast, and the trend profile is only appliedto tax returns from that region.

5. Computer Diagram and Computer Readable Medium

FIG. 4 shows a computer system 400 that may be used with the embodimentsdescribed herein. The computer system 400 represents a generic platformthat includes components that may be in a server or other computersystem. The computer system 400 may be used as a platform for the taxreturn evaluation system 100 and the tax computer 110 shown in FIG. 1,and represents a computer system configured to execute one or more ofthe methods, functions and other steps described herein. These steps maybe embodied as software stored on one or more computer readable mediums.

The tax return evaluation system 100 may also be provided as anenterprise system executed on multiple computer systems, such asmultiple servers. For example, if the tax return evaluation system 100may include an application server and a database server. Also, the taxreturn evaluation system 100 may include a web server handling requestsfrom the tax agencies 101 a-n to evaluate tax returns.

The computer system 400 includes one or more processors 402 that mayimplement or execute software instructions performing some or all of themethods, functions and other steps described herein. Commands and datafrom the processor 402 are communicated over a communication bus 404.The computer system 400 also includes a main memory 406, such as arandom access memory (RAM), where the software and data for processor402 may reside during runtime, and a secondary data storage 408, whichmay be non-volatile and stores software and data. The memory andsecondary data storage are examples of computer readable mediums.

The computer system 400 may include one or more I/O devices 410, such asa keyboard, a mouse, a display, etc. The computer system 400 may includea network interface 412 for connecting to a network. It will be apparentto one of ordinary skill in the art that other known electroniccomponents may be added or substituted in the computer system 400.

One or more of the steps of the methods described herein and other stepsdescribed herein and one or more of the components of the systemsdescribed herein may be implemented as computer code stored on acomputer readable medium, such as the memory and/or secondary storage,and executed on a computer system, for example, by a processor,application-specific integrated circuit (ASIC), or other controller. Thecode may exist as software program(s) comprised of program instructionsin source code, object code, executable code or other formats. Examplesof computer readable medium include conventional computer system RAM(random access memory), ROM (read only memory), EPROM (erasable,programmable ROM), EEPROM (electrically erasable, programmable ROM),hard drives, and flash memory.

While the embodiments have been described with reference to examples,those skilled in the art will be able to make various modifications tothe described embodiments without departing from the scope of theclaimed embodiments. Furthermore, the embodiments described herein maybe used in combination with each other.

1. A computer readable storage medium including computer code that whenexecuted by a processor performs a method of evaluating a tax return,the method comprising: storing profiles from at least one tax agency,each profile including one or more factors for evaluating tax returns;receiving a plurality of data fields from a tax return from the at leastone tax agency; comparing the plurality of data fields from the taxreturn to at least one stored profile for the at least tax agency;determining whether the tax return falls within a trend; and generatingan evaluation of the tax return based on the comparison and thedetermination of whether the tax return falls within the trend.
 2. Thecomputer readable storage medium of claim 1, wherein each of the factorsin the profiles identifies a data field in the received tax return thatis considered relevant to an objective of the evaluation.
 3. Thecomputer readable storage medium of claim 2, wherein the objective is toidentify fraudulent tax returns or misreportings in tax returns.
 4. Thecomputer readable storage medium of claim 3, wherein comparing theplurality of data fields from the tax return to the at least one storedprofile further comprises: comparing the factors in the at least onestored profile to corresponding data fields in the tax return todetermine whether values in the corresponding data fields are indicativethat the tax return is fraudulent or misreported.
 5. The computerreadable storage medium of claim 5, wherein the method furthercomprises: determining whether values in data fields in the tax returnmatch information from one or more data sources other than the taxagency, wherein the information is indicative of a fraudulent taxreturn.
 6. The computer readable of claim 1, wherein comparing pluralityof data fields from the tax return to the at least one stored profilefurther comprises: identifying data fields from the plurality of datafields from the tax return that correspond to factors in the at leastone stored profile; comparing values for the factors in the at least onestored profile to values in the corresponding data fields in the taxreturn; and determining a score for each of the corresponding datafields in the tax return based on the comparisons.
 7. The computerreadable storage medium of claim 6, wherein determining whether the taxreturn falls within a trend further comprises: determining whether aplurality of the corresponding data fields in the tax return areindicative of a fraudulent tax return based on the comparisons; and if aplurality of the corresponding data fields in the tax return areindicative of a fraudulent tax return, then determining a trend scoremultiplier.
 8. The computer readable storage medium of claim 7, whereingenerating an evaluation of the tax return further comprises:determining a final score for the tax return from the scores for thecorresponding data fields and the trend score multiplier if the taxreturn falls within the trend; and reporting the final score to theagency providing the tax return.
 9. The computer readable storage mediumof claim 7, determining a trend score multiplier further comprises:determining a trend score multiplier if a majority of the factors in theat least one profile match the corresponding data fields in the taxreturn.
 10. The computer readable storage medium of claim 7, determininga trend score multiplier further comprises: determining a trend scoremultiplier if all the factors in the at least one profile match thecorresponding data fields in the tax return.
 11. The computer readablemedium of claim method of claim 1, further comprising: determining atrend profile by identifying factors from tax returns determined tohighly likely be fraudulent; and storing the trend profile, wherein thetrend profile includes the factors from tax returns determined to highlylikely be fraudulent.
 12. The computer readable medium of claim 11,wherein the method further comprises: comparing the factors in the trendprofile to the tax return to determine the evaluation of the tax return.13. A tax return evaluation system comprising: a database storingprofiles from a plurality of agencies, each profile including one ormore factors for evaluating tax returns; a computer system configured tocompare a plurality of data fields from each of a plurality of taxreturns to a stored profile and is configured to determine whether thetax returns fall within a trend, wherein the computer system is furtherconfigured to generate an evaluation of each received tax return basedon a comparison of the plurality of data fields in each tax return thatcorrespond to factors in the stored profile and based on thedetermination of whether each tax return falls within the trend.
 14. Thetax return evaluation system of claim 13, wherein the computer systemdetermines a tax return falls within the trend if multiple data fieldsin the tax return that correspond to factors in the stored profile areindicative of fraud.
 15. The tax return evaluation system of claim 13,wherein the evaluation is a score indicative of a likelihood of fraudfor each tax return.
 16. The tax return evaluation system of claim 15,wherein the score is calculated by determining a score for each factorin the stored profile based on a value for the corresponding data field,and combining the scores to determine the evaluation for the tax return.17. The tax return evaluation system of claim 16, wherein the evaluationis further determined using a trend score multiplier if the tax returnfalls within the trend.
 18. The tax return evaluation system of claim13, wherein the computer system comprises a web server configured toreceive tax returns from the plurality of agencies via the Internet andreport the evaluation for each tax return to the agency sending the taxreturn.
 19. The tax return evaluation system of claim 13, wherein thecomputer system is connected to a plurality of data sources differentfrom the plurality of tax agencies, and the data sources provideinformation for determining the evaluation of each tax return.
 20. Amethod of evaluating tax returns comprising: storing profiles from atleast one tax agency in a data storage device, each profile includingone or more factors for evaluating tax returns; receiving a plurality ofdata fields from a tax return from the at least one tax agency;comparing the plurality of data fields from the tax return to at leastone stored profile provided by the at least one tax agency and to atrend profile; and comparing at least some of the plurality of datafields from each tax return to the trend profile; and generating anevaluation of each tax return based on the comparison to the at leastone stored profile and the trend profile, wherein the evaluation is anindication of the tax return's potential to be a fraudulent tax return.